In addition, in real-world applications, the test distributions are usually unknown to us, and the classes are often long-tailed distributed because of Zipf's law of nature, e.g., cancer detection.
Evaluation under long-tailed test set is closer to the wild.
Note that this setting is different from [1]}, where the test class prior is known during the training stage. In our setting, no side information about the test distribution prior is accessible to the models.
